(Data-driven) knowledge representation in Industry 4.0 scheduling problems

Author(s):  
Daniel A. Rossit ◽  
Fernando Tohmé
Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2813
Author(s):  
Muslikhin Muslikhin ◽  
Jenq-Ruey Horng ◽  
Szu-Yueh Yang ◽  
Ming-Shyan Wang ◽  
Baiti-Ahmad Awaluddin

In this study, an Artificial Intelligence of Things (AIoT)-based automated picking system was proposed for the development of an online shop and the services for automated shipping systems. Speed and convenience are two key points in Industry 4.0 and Society 5.0. In the context of online shopping, speed and convenience can be provided by integrating e-commerce platforms with AIoT systems and robots that are following consumers’ needs. Therefore, this proposed system diverts consumers who are moved by AIoT, while robotic manipulators replace human tasks to pick. To prove this idea, we implemented a modified YOLO (You Only Look Once) algorithm as a detection and localization tool for items purchased by consumers. At the same time, the modified YOLOv2 with data-driven mode was used for the process of taking goods from unstructured shop shelves. Our system performance is proven by experiments to meet the expectations in evaluating efficiency, speed, and convenience of the system in Society 5.0’s context.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanjana Mondal ◽  
Kaushik Samaddar

PurposeThe paper aims to explore the various dimensions of human factor relevant for integrating data-driven supply chain quality management practices (DDSCQMPs) with organizational performance. Keeping the transition phase from “Industry 4.0” to “Industry 5.0” in mind, the paper reinforces the role of the human factor and critically discusses the issues and challenges in the present organizational setup.Design/methodology/approachFollowing the grounded theory approach, the study arranged in-depth interviews and focus group sessions with industry experts from various service-oriented firms in India. Dimensions of human factor identified from there were grouped together through a morphological analysis (MA), and interlinkages between them were explored through a cross-consistency matrix.FindingsThis research work identified 20 critical dimensions of human factor and have grouped them under five important categories, namely, cohesive force, motivating force, regulating force, supporting force and functional force that drive quality performance in the supply chain domain.Originality/valueIn line with the requirements of the present “Industry 4.0” and the forthcoming “Industry 5.0”, where the need to collaborate human factor with smart system gets priority, the paper made a novel attempt in presenting the critical human factors and categorizing them under important driving forces. The research also contributed in linking DDSCQMPs with organizational performance. The proposed framework can guide the future researchers in expanding the theoretical constructs through initiating further cross-cultural studies across industries.


2021 ◽  
pp. 1-23
Author(s):  
Daniel Alejandro Rossit ◽  
Adrián Toncovich ◽  
Diego Gabriel Rossit ◽  
Sergio Nesmachnow

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